This algorithm statement is adapted and simplified for our purposes;
readers interested in the original version should consult [1]}.
We say that \(\mathcal {X}\)  is our data universe, \(\mathcal {D}\)  is a distribution
over \(\mathcal {X}\) , and our realized data \(X \in \mathbb {R}^{n \times m}\)  are drawn from
\(\mathcal {D}^n\) . For an arbitrary estimator \(\hat{\tau }: \mathcal {X}^n \rightarrow \mathbb {R}^d\) ,
we define \(\hat{\tau }(\mathcal {D}) = \operatornamewithlimits{\mathbb {E}}_{X \sim \mathcal {D}^n} \left( \hat{\tau }(X) \right)\) .
